Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook
For researchers studying multi-agent AI systems, this work provides a method to characterize emotional consistency, but it is an incremental application of existing emotion analysis techniques to a specific platform.
This paper analyzes emotional dynamics in agent-to-agent interactions on the Moltbook social network, introducing an emotion-aware framework and the Persona-Stimulus-Reaction (PSR) domain to extract emotion profiles and evaluate behavioral reliability. The analysis reveals distinct emotional signatures and varying behavioral stability across agents.
Generative AI systems are increasingly deployed as interactive agents in online environments, such as a social network called Moltbook. In Moltbook, large-scale agentic AIs can post, comment, and engage in activities generated at scale by AI-driven text. Yet these agent behavioral characteristics remain insufficiently understood, particularly in complex, multi-agent interaction. In this study, we analyze the emotional dynamics of agent interactions within Moltbook. We construct an emotion-aware framework that maps textual interactions to a predefined set of fine-grained emotional categories, enabling the extraction of structured emotion profiles across agents and interaction contexts. To further evaluate behavioral reliability, we introduce an emotion-based domain called Persona-Stimulus-Reaction (PSR) that captures the alignment of emotional responses across similar contexts. Our analysis shows distinct emotional patterns and varying levels of behavioral stability across agents. Our analysis reveals that agents exhibit distinct emotional signatures with varying levels of behavioral stability influenced by interaction context.